A model-based prognostics method for fatigue crack growth in aircraft fuselage panels

This paper proposes a model-based prognostics method that couples the Extended Kalman filter (EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack propagation in fuselage panels where the model parameters are unknown and the crack propagation is affected by different types of uncertainties. The coupled method is composed of two steps. The first step employs EKF to estimate the unknown model parameters and the current damage state. In the second step, the proposed efficient linearization method is applied to compute analytically the statistical.distribution of the damage evolution path in some future time. A numerical case study is implemented to evaluate the performance of the proposed method. The results show that the coupled KEF-linearization method provides satisfactory results: (1) the EKF algorithm well.identifies the model parameters, and (2) the linearization method gives comparable prediction results to Monte Carlo while leading to very significant computational cost saving. The proposed prognostics method for fatigue crack growth can be used for developing predictive maintenance strategy for an aircraft fleet, in which case, the computational cost saving is significantly meaningful.

本文提出了一种基于模型的预测方法，该方法将扩展卡尔曼滤波器（EKF）与一种新开发的线性化方法相结合。所提出的预测方法是在机身面板疲劳裂纹扩展的背景下开发的，其中模型参数未知，并且裂纹扩展受到不同类型不确定性的影响。耦合方法由两个步骤组成。第一步使用EKF来估计未知的模型参数和当前的损伤状态。在第二步中，应用所提出的高效线性化方法来解析计算未来某个时间损伤演化路径的统计分布。通过一个数值案例研究来评估所提方法的性能。结果表明，耦合的EKF - 线性化方法提供了令人满意的结果：（1）EKF算法能很好地识别模型参数，（2）线性化方法给出的预测结果与蒙特卡洛方法相当，同时大大节省了计算成本。所提出的疲劳裂纹扩展预测方法可用于制定飞机机队的预测性维护策略，在这种情况下，计算成本的节省具有重要意义。